prioritizr / prioritizr
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@@ -288,19 +288,25 @@
 288 288 ` assertthat::assert_that(` 289 289 ` inherits(x, "ConservationProblem"),` 290 290 ` is.matrix(solution))` 291 + ` # convert NAs in solution to zeros` 292 + ` solution[is.na(solution)] <- 0` 291 293 ` # calculate amount of each feature in each planning unit` 292 294 ` total <- x\$feature_abundances_in_total_units()` 293 - ` held <-` 294 - ` vapply(` 295 - ` seq_len(x\$number_of_zones()),` 296 - ` FUN.VALUE = numeric(nrow(x\$data\$rij_matrix[[1]])),` 297 - ` function(i) {` 298 - ` rowSums(` 299 - ` x\$data\$rij_matrix[[i]] *` 300 - ` matrix(solution[, i], ncol = nrow(solution),` 301 - ` nrow = nrow(x\$data\$rij_matrix[[1]]), byrow = TRUE),` 302 - ` na.rm = TRUE)` 303 - ` })` 295 + ` held <- vapply(` 296 + ` seq_len(x\$number_of_zones()),` 297 + ` FUN.VALUE = numeric(nrow(x\$data\$rij_matrix[[1]])),` 298 + ` function(i) {` 299 + ` as.numeric(` 300 + ` x\$data\$rij_matrix[[i]] %*%` 301 + ` Matrix::Matrix(` 302 + ` solution[, i],` 303 + ` ncol = 1,` 304 + ` nrow = nrow(solution),` 305 + ` sparse = TRUE` 306 + ` )` 307 + ` )` 308 + ` }` 309 + ` )` 304 310 ` # prepare output` 305 311 ` if (x\$number_of_zones() == 1) {` 306 312 ` out <- tibble::tibble(`
@@ -310,8 +316,8 @@
 310 316 ` absolute_held = unname(c(held)),` 311 317 ` relative_held = unname(c(held / total)))` 312 318 ` } else {` 313 - ` total <- c(rowSums(total), c(total))` 314 - ` held <- c(rowSums(held), c(held))` 319 + ` total <- c(Matrix::rowSums(total), c(total))` 320 + ` held <- c(Matrix::rowSums(held), c(held))` 315 321 ` out <- tibble::tibble(` 316 322 ` summary =` 317 323 ` rep(c("overall", x\$zone_names()), each = x\$number_of_features()),`

@@ -931,14 +931,14 @@
 931 931 ` if (is.null(names(rij_matrix)))` 932 932 ` names(rij_matrix) <- as.character(seq_along(rij_matrix))` 933 933 ` # calculate feature abundances in total units` 934 - ` fatu <- vapply(rij_matrix, rowSums, numeric(nrow(rij_matrix[[1]])),` 934 + ` fatu <- vapply(rij_matrix, Matrix::rowSums, numeric(nrow(rij_matrix[[1]])),` 935 935 ` na.rm = TRUE)` 936 936 ` if (!is.matrix(fatu))` 937 937 ` fatu <- matrix(fatu, nrow = nrow(features), ncol = length(rij_matrix))` 938 938 ` rownames(fatu) <- as.character(features\$name)` 939 939 ` colnames(fatu) <- names(rij_matrix)` 940 940 ` # convert rij matrices to sparse format if needed` 941 - ` pos <- which(rowSums(!is.na(x)) > 0)` 941 + ` pos <- which(Matrix::rowSums(!is.na(x)) > 0)` 942 942 ` rij <- lapply(rij_matrix, function(z) {` 943 943 ` if (inherits(z, "dgCMatrix")) {` 944 944 ` z@x[which(is.na(z@x))] <- 0`
@@ -1013,7 +1013,7 @@
 1013 1013 ` return(m)` 1014 1014 ` })` 1015 1015 ` # calculate feature abundances in total units` 1016 - ` fatu <- vapply(rij, rowSums, numeric(number_of_features(features)),` 1016 + ` fatu <- vapply(rij, Matrix::rowSums, numeric(number_of_features(features)),` 1017 1017 ` na.rm = TRUE)` 1018 1018 ` if (!is.matrix(fatu))` 1019 1019 ` fatu <- matrix(fatu, nrow = number_of_features(features),`
@@ -1021,8 +1021,9 @@
 1021 1021 ` rownames(fatu) <- feature_names(features)` 1022 1022 ` colnames(fatu) <- zone_names(features)` 1023 1023 ` # create rij matrix` 1024 - ` pos <- which(rowSums(!is.na(as.matrix(` 1025 - ` x2[, cost_column, drop = FALSE]))) > 0)` 1024 + ` pos <- which(` 1025 + ` rowSums(!is.na(as.matrix(x2[, cost_column, drop = FALSE]))) > 0` 1026 + ` )` 1026 1027 ` rij <- lapply(rij, function(x) x[, pos, drop = FALSE])` 1027 1028 ` names(rij) <- zone_names(features)` 1028 1029 ` # create ConservationProblem object`
 1 ```coverage: ``` 2 ``` status: ``` 3 ``` project: ``` 4 ``` default: ``` 5 ``` target: auto ``` 6 ``` threshold: 20% ``` 7 ``` patch: ``` 8 ``` default: ``` 9 ``` target: auto ``` 10 ``` threshold: 20% ``` 11 ``` informational: true ```